Instabilities of nanofluid flow displacements in porous media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thanks to a number of advantageous characteristics, nanofluids are widely used in a variety of fluid flow systems. In porous media flows, the presence of nanoparticles can have dramatic effects on the flow dynamics and in particular on viscous fingering instabilities that develop when a less viscous fluid displaces a more viscous one. In the present study, these effects have been investigated both analytically and numerically using linear stability analysis (LSA) and non-linear simulations. The LSA problem was solved analytically using step function approximation, and general conclusions about the effects of nanofluids on the instability were derived from long wave expansion and cutoff wave number analyses. Furthermore, the quasi-steady-state approximation was used to expand the results of the LSA to diffusing initial concentration profiles, and simulations of the full non-linear problem have been carried out using a Hartley-transform based pseudo-spectral method. Results revealed that nanoparticles cannot make an otherwise stable flow unstable but can enhance or attenuate the instability of an originally unstable flow. In particular it was found that increases in the nanoparticles deposition rate or their rate of diffusion have both destabilizing effects. Furthermore, nanoparticles deposition can change the initial monotonically decreasing viscosity distribution to a non-monotonic one and results in the development of vortex dipoles. Analyses of vortex structures along with the viscosity distributions allowed to explain the observed trends and the resulting finger configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it